sickboy06/global-ev-infra-dataset

🌍 Global EV Charging Stations & EV Models Dataset

Author: Tarek Masryo · Kaggle
Version: v1.0 (2025-09-15)
License: CC BY 4.0

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📌 TL;DR

A clean, analysis-ready dataset capturing the state of EV infrastructure in 2025:

• 242,417 rows across 121 countries
• 11 tidy columns describing charging sites
• Companion files: country/world summaries + EV models

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🚗 Why this dataset?

EV adoption is accelerating, but infrastructure data is fragmented and inconsistent.
This dataset delivers a global, standardized snapshot of charging availability, enabling:

• EV adoption & policy analysis
• Energy & sustainability research
• Machine learning & dashboard prototyping

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📂 Files Included

• data/charging_stations_world.csv — global stations (main file, 11 columns)
• data/charging_stations_ml.csv — ML-ready, compact version (7 columns)
• data/country_summary.csv — per-country roll-up
• data/world_summary.csv — global roll-up
• data/ev_models.csv — EV model specifications
• OCM_CC_BY_4.0.txt — license text

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🗄️ Data Dictionary

charging_stations_world.csv

Column	Type	Description
id	int	Unique station ID (OCM)
name	str	Station name
city	str	City name (may be "UNKNOWN")
country_code	str	ISO-2 country code
state_province	str	State/Province (may be "UNKNOWN")
latitude	float	WGS84 latitude
longitude	float	WGS84 longitude
ports	int	Number of charging points
power_kw	float	Maximum charging power (kW)
power_class	str	Derived category (slow/fast/HPC)
is_fast_dc	bool	True if power_kw ≥ 50

country_summary.csv

Column	Type	Description
country_code	str	ISO-2 country code
stations	int	Number of charging stations

world_summary.csv

Column	Type	Description
country_code	str	ISO-2 country code
country	str	Country name
count	int	Number of charging sites
max_power_kw_max	float	Max observed charging power (kW)

ev_models.csv

Column	Type	Description
make	str	Manufacturer
model	str	Model name
market_regions	str	Regions where model is sold
powertrain	str	BEV, PHEV, etc.
first_year	int	First year released
body_style	str	Sedan, SUV, etc.
origin_country	str	Manufacturer country

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🛠️ Quickstart

Using pandas:

import pandas as pd

stations = pd.read_csv("data/charging_stations_world.csv")
print(stations.shape)
print(stations.head())

Using Hugging Face Datasets:

from datasets import load_dataset

ds = load_dataset("tarekmasryo/global-ev-infra-data")

# Access main stations file
world = ds["charging_stations_world"]
print(world[0])

# Access country summary
country = ds["country_summary"]
print(country[0])

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💡 Suggested Uses

• Compare EV infrastructure across regions
• Measure share of fast-DC vs slow charging
• Build EV adoption dashboards
• Train ML models (clustering, forecasting, location analysis)
• Prototype routing/location tools for EV drivers

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📜 License & Attribution

• Charging station data: © Open Charge Map — CC BY 4.0
  → “Contains data © Open Charge Map contributors.”
• EV models file: compiled from CC0-friendly sources (no attribution required).